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Updated: Jul 13, 2026
Movement Retraining using Real-time Feedback of Performance
Published on: January 17, 2013
Multimodal Integration of Gait, Balance, and Infrared Thermography Enhances Machine Learning Classification of Knee
Federico Roggio1, Martina Sortino1, Bruno Trovato1
1Department of Biomedical and Biotechnological Sciences, Section of Anatomy, Histology and Movement Science, School of Medicine, University of Catania, Catania, Italy.
Objective:
To investigate whether combining gait, balance, and infrared thermography improves the discrimination between individuals with knee osteoarthritis (KOA) and those without KOA compared with single-domain models.
Design:
Cross-sectional diagnostic study with supervised machine learning analysis using repeated 10×5 cross-validation and paired ROC comparisons.
Setting:
Research laboratories, orthopaedics unit, and physical medicine and rehabilitation unit.
Participants:
Fifty-five adults (N=55) aged 45-80 years were included: 28 participants with clinically and radiographically confirmed KOA and 27 HC. KOA severity was graded using the Kellgren-Lawrence scale. HC had no history of knee pain, injury, or surgery.
Interventions:
Not applicable.
Main Outcome Measures:
Spatiotemporal gait parameters, postural stability measures, knee surface temperatures, and classification performance of gait-only, balance-only, movement-only, thermal-only, and multimodal SVM models, assessed by area under the curve (AUC), sensitivity, and specificity.
Results:
Compared with HC, KOA participants showed slower walking speed (adjusted P=.030), reduced gait symmetry (adjusted P=.018), and larger sway ellipse area, especially under eyes closed conditions (adjusted P=.003). Thermography showed consistently higher temperatures in KOA, particularly in the lower popliteal fossa (31.11±0.99°C). Among the top 10 models, the thermal-only model achieved perfect discrimination (AUC=1.000), whereas the multimodal model also showed excellent performance (AUC=0.988; sensitivity=0.937; specificity=0.887). Paired ROC comparisons showed that the multimodal model significantly outperformed gait-only, balance-only, and movement-only models, while not differing from the thermal-only model.
Conclusions:
KOA is characterized by concurrent gait, balance, and thermal alterations. Although thermography alone showed the highest discriminative performance in this sample, multimodal integration provided a broader and clinically coherent framework for KOA classification.